ML Security PapersState-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects
Harshika Goyal 1, Mohammad Saif Wajid 2, Mohd Anas Wajid 3, Akib Mohi Ud Din Khanday 4,5, Mehdi Neshat 6,7, Amir Gandomi 6,8
1 Indian Institute of Technology
4 United Arab Emirates University
5 Samarkand International University of Technology
Published on arXiv
2501.01029
Output Integrity Attack
OWASP ML Top 10 — ML09
Key Finding
Comprehensive taxonomy of deepfake generation and detection approaches reveals that while generation quality has become near-photorealistic, detection methods still face significant challenges in generalization across manipulation types and unseen forgery techniques.
The rapid advancement of deepfake technologies, specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across many fields, including forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methods, such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformers, the outcomes achieved in generating deepfakes have been nothing short of astounding and transformative. Also, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Subsequently, the paper is dedicated to accurately benchmarking leading approaches against prominent datasets, offering thorough assessments of the contributions that have significantly impacted these vital domains. Ultimately, we engage in a thoughtful discussion of the existing challenges, paving the way for continuous advancements in this critical and ever-dynamic study area.
Key Contributions
- Systematic literature review of ~400 publications covering deepfake generation and detection techniques
- Comprehensive analysis of GAN, VAE, Few-Shot Learning, and Transformer-based deepfake generation methods
- Benchmarking of leading detection approaches against prominent datasets with discussion of open challenges and future directions
🛡️ Threat Analysis
Deepfake detection is a canonical ML09 (Output Integrity) problem — detecting and authenticating AI-generated facial imagery and video content falls squarely under AI-generated content detection and output provenance verification.